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http://dx.doi.org/10.6109/jkiice.2020.24.6.700

GAN System Using Noise for Image Generation  

Bae, Sangjung (Department of Computer Engineering, Paichai University)
Kim, Mingyu (Department of Computer Engineering, Paichai University)
Jung, Hoekyung (Department of Computer Engineering, Paichai University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.6, 2020 , pp. 700-705 More about this Journal
Abstract
Generative adversarial networks are methods of generating images by opposing two neural networks. When generating the image, randomly generated noise is rearranged to generate the image. The image generated by this method is not generated well depending on the noise, and it is difficult to generate a proper image when the number of pixels of the image is small In addition, the speed and size of data accumulation in data classification increases, and there are many difficulties in labeling them. In this paper, to solve this problem, we propose a technique to generate noise based on random noise using real data. Since the proposed system generates an image based on the existing image, it is confirmed that it is possible to generate a more natural image, and if it is used for learning, it shows a higher hit rate than the existing method using the hostile neural network respectively.
Keywords
Machine Learning; Generative Advesarial Networks; Semi-Supervised Learning; DCGAN; GAN;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
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